Systems and methods to facilitate local searches via location disambiguation

ABSTRACT

Systems and methods use machine learning techniques to resolve location ambiguity in search queries. In one aspect, a dataset generator generates a training dataset using query logs of a search engine. A training engine applies a machine learning technique to the training dataset to generate a location disambiguation model. A location disambiguation engine uses the location disambiguation model to resolve location ambiguity in subsequent search queries.

FIELD OF THE TECHNOLOGY

At least some embodiments of the disclosure relate to locationdisambiguation in general and more particularly, but not limited to,location disambiguation for search engines.

BACKGROUND

An unambiguous location reference can be uniquely located. An ambiguouslocation reference corresponds to more than one location. For example,the term “Springfield” by itself can refer to 30 different cities in theUSA. While “Springfield” is an ambiguous location, “Springfield, IL,USA” is an unambiguous location reference.

The location disambiguation problem has been studied in the context oflarge textual documents. Benefiting from the broader context that can bederived from the neighboring text, prior methods for locationdisambiguation in the context of large textual documents often usead-hoc rules.

For example, H. Li, R. K. Srihari, C. Niu, and W. Li (“InfoXtractLocation Normalization: A Hybrid Approach to Geographic References inInformation Extraction,” Proceedings of the HLT-NAACL 2003 Workshop onAnalysis of Geographic References, Volume 1, Association forComputational Linguistics, 2003, pp. 39-44) assume “one sense perdiscourse,” in that subsequent mention of a location reference can beidentified with the first unambiguous location reference.

Similarly, G. Andogah, G. Bouma, J. Nerbonne, and E. Koster(“Geographical Scope Resolution,” Methodologies and Resources forProcessing Spatial Language, 2008) use spatial proximity as a criterionfor disambiguating ambiguous location references, which assumes that“places of the same type or under the same administrative jurisdictionor near/adjacent to each other are more likely to be mentioned in agiven discourse.”

Alternatives to these rule-based approaches are topological andontological based approaches, which benefit from certain inherentproperties of an unambiguous location reference.

For example, an unambiguous location reference is a composite of variousgeographic entities (e.g., city, state, country, etc.) that are presentat different geographic scales. In the example of “Springfield, IL,USA,” there are three geographic entities, which are “Springfield,”“IL,” and “USA.” These entities exhibit a containment relationship in away that the city Springfield is located in the state of IL and IL islocated in the USA.

Based on this insight, V. Sengar, T. Joshi, J. Joy, S. Prakash, and K.Toyama (“Robust Location Search from Text Queries,” Proceedings of the15th Annual ACM International Symposium on Advances in GeographicInformation Systems, ACM, 2007, pp. 1-8) formalize the problem oflocation disambiguation as the challenge of finding a region that is aspatial intersection of all the geographic entities present in a givenlocation reference. For example, consider the location reference of“Mission Street, San Francisco.” This location reference has two parts,i.e. “Mission Street” and “San Francisco,” which have separaterepresentations in a geographic database. Each of the above entitieswill lead to many possible geographic entities. However, in thebest-case scenario, the spatial intersection of all these possibleentities will only lead to a single intended region.

B. Martins, M. J. Silva, S. Freitas, and A. P. Afonso (“HandlingLocations in Search Engine Queries,” Proceeding of the 3rd ACM Workshopon Geographic Information Retrieval, 2006) and R. Volz, J. Kleb, and W.Mueller (“Towards Ontology-Based Disambiguation of GeographicIdentifiers,” Proceedings of 16th International Conference on World WideWeb, 2007) use approaches that model the location disambiguation problemas one of finding a single branch that contains all the geographicentities mentioned in a given location reference and thus usegeographical ontology, instead of topological operations, to find theintended location. G. Fu, C. B. Jones, and A. I. Abdelmoty(“Ontology-Based Spatial Query Expansion in Information Retrieval,” InLecture Notes in Computer Science, Volume 3761, On the Move toMeaningful Internet Systems: Odbase, 2005, pp. 1466-1482) and T.Kauppinen, R. Henriksson, R. Sinkkilä, R. Lindroos, J. Väätäinen, and E.Hyvönen (“Ontology-Based Disambiguation of Spatiotemporal Locations,”Proceedings of the 1st International Workshop on Identity and Referenceon the Semantic Web (IRSW2008), 5th European Semantic Web Conference,2008) provide further examples.

There are several challenges with the topological and ontologicalapproaches. One challenge is that the operation of breaking a locationreference into its constituent geographic entities is a combinatorialexplosion problem. I. Jenhani, N. B. Amor, and Z. Elouedi (“DecisionTrees as Possibilistic Classifiers,” International Journal ofApproximate Reasoning Vol. 48, No. 3, 2008, pp. 784-807) address thischallenge to some extent.

However, there is another challenge when location references areincomplete (such as “San Jose” or “Springfield”), such that there aremultiple spatial regions or ontological branches corresponding to alocation reference. Some approaches use ad-hoc heuristic rules to rankthese competing locations, such as in R. Volz, J. Kleb, and W. Mueller(“Towards Ontology-Based Disambiguation of Geographical Identifiers,”Proceedings of 16th International Conference on World Wide Web, 2007),which rank competing ontological branches based on certain empiricallydecided weights that are based on structural features (such as featureclass, population, etc.) that are available in a geographic gazetteer.

An alternate to such rule-based approaches is in D. A. Smith and G. S.Mann (“Bootstrapping Toponym Classifiers,” Proceedings of the HLT-NAACL2003 Workshop on Analysis of Geographic References—Volume 1, Associationfor Computational Linguistics, 2003, pp. 45-49), which proposes adata-driven place name classifier that is trained and tested on newsarticles and historical documents.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 shows a system to provide local searches according to oneembodiment.

FIG. 2 shows a method and system to generate a location model accordingto one embodiment.

FIG. 3 illustrates training features for a location model according toone embodiment.

FIG. 4 shows a method and system to generate target values of a trainingdataset for a location model according to one embodiment.

FIG. 5 shows a method to resolve location ambiguity according to oneembodiment.

FIG. 6 shows a data processing system to implement components andsystems according to various embodiments.

DETAILED DESCRIPTION

The following description and drawings are illustrative and are not tobe construed as limiting. Numerous specific details are described toprovide a thorough understanding. However, in certain instances, wellknown or conventional details are not described in order to avoidobscuring the description. References to one or an embodiment in thepresent disclosure are not necessarily references to the sameembodiment; and, such references mean at least one.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the disclosure. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment, and separate or alternative embodiments are notmutually exclusive of other embodiments. Moreover, various features aredescribed which may be exhibited by some embodiments and not by others.Similarly, various requirements are described which may be requirementsfor some embodiments but not other embodiments.

Local queries, or local searches, such as “pizza but in glendale, CA,”“Chinese restaurant near golden gate bridge,” etc., are geographicallyconstrained searches. For example, a search engine in one embodiment isconfigured to search a structured database for information such as localbusiness listings, advertisements, news, articles, events, etc.

Typical local search queries include not only topic information about“what” the site visitor is searching for (such as keywords, a businesscategory, or the name of a consumer product) but also locationinformation about “where,” such as a street address, city name, postalcode, or geographic coordinates like latitude and longitude. Examples oflocal searches include “plumbers in noe valley,” “Manhattanrestaurants,” etc.

Uniquely locating the referred geographical constraint on the surface ofthe Earth and thus determining the geographical intent of the queryhelps to address local queries effectively. However, geographicalconstraints in local queries are often incomplete and therefore sufferfrom the problem of location ambiguity, where the same locationreference leads to many different possible locations.

For example, consider a local search for “Italian restaurants inSpringfield,” where the term “Springfield” by itself can refer to 30different cities in the USA. Without any other contextual information,this search can relate to “Springfield, IL,” “Springfield, MO,” or other“Springfield.”

In one embodiment, a method and system use a formalized data-drivenapproach for location disambiguation in local searches. One embodimentof the disclosure provides location models for the determination of thegeographic intent of local queries or searches. At least one embodimentin the present disclosure provides systems and methods to use gradientboosted decision trees for location disambiguation in local searches.

In one embodiment, the method and system use machine learningtechniques, such as gradient boosted decision trees, to build and trainlocation models based on contextual features and/or non-contextualfeatures.

In one embodiment, the method and system provide location disambiguationfor local searches in different search environments, such as mobilesearches (e.g., search requests from mobile devices) and desktopsearches (e.g., search requests from non-mobile devices having fixedlocations).

In one embodiment, the method and system not only improve geographicretrieval performance but also provide insight into the relativeinfluence of various features for location disambiguation in differentsearch environments.

Heuristics based systems to disambiguate locations in large textualdocuments, such as a webpage, a Wikipedia article or a news article, maynot be suitable for location disambiguation in local searches. Forexample, ad-hoc rules of such systems may be subject to the limitationof the intuition of their designers. Even ontological and topologicalbased systems involve ad-hoc rules to address multiple competinglocation possibilities.

Ad-hoc rules are subject to the limitation of the experience andintuitions of the developer, and are often situation dependent. Forexample, rules designed to help disambiguate locations in desktop-basedlocal searches might not be applicable to mobile-based local searches.

Further, while location disambiguation in large textual documentsbenefits from the wider context that can be derived from neighboringwords, sentences and other unambiguous location references in the text,such context information is typically absent in local searches. Largetextual documents can provide useful clues from other locationreferences in the text for the disambiguation of a given ambiguouslocation reference. However, local searches are usually very short. Forexample, M. Sanderson and J. Kohler (“Analyzing Geographic Queries,”Proceedings of the Workshop on Geographic Information Retrieval (SIGIR,2004)) indicate that the average number of words per local query isabout 3.3. As a result, disambiguation algorithms for local searches donot have access to the contextual information that can be derived from alarge body of text.

In one embodiment, a location disambiguation system and method use asystematic, formalized and data-driven approach. The system uses machinelearning techniques, such as generalized linear models or gradientboosted decision trees, to build location models from data recorded fromprior searches.

In one embodiment, the location disambiguation system uses variousfeatures from the local search query log of a search engine and/or andfeatures from other resources, such as geographic databases andgazetteers, the US Census, etc. to train the location models usingmachine learning techniques.

One embodiment of the disclosure uses a pair-wise machine learningtechnique in training the location model. Another embodiment of thedisclosure uses a point-wise machine learning technique in training thelocation model. C. Burges, T. Shaked, and E. Renshaw (“Learning to RankUsing Gradient Descent,” Proceedings of the 22nd InternationalConference on Machine Learning, ACM, 2005, pp. 89-96) provide detailsabout a point-wise machine learning technique, the disclosure of whichis incorporated herein by reference.

The systems and methods according to embodiments presented in thisdisclosure have advantages over the past approaches. For example, thesystems and methods according to embodiments presented in thisdisclosure are not based on ad-hoc, a priori, heuristic rules. Thesystems and methods according to embodiments presented in thisdisclosure use a machine learning technique to automatically generatethe decision rules based on training data. Further, the systems andmethods according to one embodiment incorporate features that cause asearch to provide different results based on the current locations ofthe searchers and other contextual information, such as past searches,to provide personalized resolution of ambiguous locations. Thus, thesame location reference (such as Springfield) might resolve to twodifferent locations (such as Springfield, Ill. and Springfield, Mo.) intwo different user contexts.

One embodiment of the system and method has been implemented and testedwithin a lab environment. In comparison to a rule based system, thisimplementation reduces error rate by almost 9% in the case ofdesktop-based local searches and 22% in the case of mobile-based localsearches. Further, the decision tree of the location model implementedin the lab environment shows the relative influence of variousgeographic and non-geographic features for location disambiguation.While the location model confirms that the distance between the user andthe intended location is an important variable for locationdisambiguation, the location model shows that the relative influence ofsuch a distance is secondary to an indicator of the popularity of thelocation in the location model.

Often geographic distance has been considered to be the most importantfeature in location searches, especially in the context of mobile-basedlocal searches. For example, R. Jones, W. V. Zhang, B. Rey, P. Jhala,and E. Stipp (“Geographic Intention and Modification in Web Search,”International Journal of Geographic Information Science, Vol., 22, No.3, 2008, pp. 229-246) characterize local searches based on geographicdistance between the user and their intended search location. Suchstudies focus on the geographic distance independent of other contextualfeatures such as popularity, population, etc. of the candidate location.

The location model implemented and tested within the lab environmentprovides insights into the influence of various features. The knowledgeobtained from inspecting the location model can improve heuristic-rulebased systems. For example, the location model shows that the parameterof raw search hit value of a location candidate has more influence thanother parameters, such as the distance between the searcher and thelocation candidates. The raw search hit value, obtained by submittingthe location candidate to a search engine to obtain a count of itemsidentified by the search engine to be relevant to the locationcandidate, appears to capture the notion of popularity of a geographicentity. Additionally, in contrast to the general belief that thedistance between the searcher and the location candidate is the mostimportant parameter in the context of a mobile search environment, thelocation model implemented and tested within the lab environment showsthat the distance is secondary to raw search hit value in both a mobilesearch environment and a desktop search environment.

However, relative influences do not provide the complete picture. Thedecision tree of the location model implemented and tested within thelab environment provides improved understanding of the role of differentparameters. In the location model implemented and tested within the labenvironment, the distance provides the first level of decision aboutwhether a given candidate location is correct or not. This correlateswith the general intuition that distance plays a major role in localsearches. However, at the lower levels of both the decision trees, rawsearch hit value becomes a major feature in deciding relevance of agiven candidate location.

FIG. 1 shows a system to provide local searches according to oneembodiment. In FIG. 1, a search engine (111) receives search queriesfrom user terminals (107, . . . , 108) and mobile devices (105, . . . ,106) via the network (101) and the wireless access points (103). Thesearch engine (111) has a database (113) that stores content index(125). The search engine (111) uses the content index (125) to generatesearch results responsive to the search queries.

In one embodiment, the search engine (111) accepts search queries thathave location ambiguities. A location disambiguation engine (109)communicates with the search engine (111) and uses a location model(127) to resolve the location ambiguities in the search queries for thesearch engine (111). In some embodiments, the location disambiguationengine (109) is part of the search engine (111). In some embodiments,the location disambiguation engine (109) is distinct and separate fromthe search engine (111).

In one embodiment, the search engine (111) is to record search queriesin query logs (121) in the database (113).

In one embodiment, the search engine (111) is to further record userinteraction with the search results in response logs (123) in thedatabase (113). In one embodiment, the response logs (123) provideinformation confirming the search intent of the queries. For example,when the user or the searcher selects a business from the search result,the selection indicates that the location of the business corresponds tothe geographical intent of the query.

In one embodiment, the location disambiguation engine (109) generatesthe location model (127) based on the query logs (121) and the responselogs (123) of the search engine (111), or similar data from anothersearch engine or a plurality of search engines.

In one embodiment, the location disambiguation engine (109) furthersubmits queries to the search engine (111) (or another search engine)and obtain search results (e.g., search hits of an unambiguous locationreference) for location disambiguation.

In one embodiment, a user terminal (e.g., 107 or 108) or mobile device(e.g., 105 or 106) is a data processing system, such as a notebookcomputer, a personal computer, a workstation, a network computer, apersonal digital assistant (PDA), a mobile phone, a cellular phone,microprocessor-based or programmable consumer electronics, and the like.

In one embodiment, a user terminal (e.g., 107 or 108) has asubstantially fixed location and thus is a non-mobile device. Forexample, a user terminal (e.g., 107 or 108) may be desktop computer. Forexample, in one embodiment, the IP address of the user terminal (e.g.,107 or 108) corresponds to a geographical location of the user terminal(e.g., 107 or 108).

In one embodiment, a mobile device (e.g., 105 or 106) has a locationdetermination device, such as a Global Positioning System (GPS)receiver, to determine and report its location. Alternatively, acellular communications system determines the location of the mobiledevice (e.g., 105 or 106) for the mobile device.

In one embodiment, when a searcher submits a search query from a userterminal (e.g., 107 or 108) or a mobile device (e.g., 105 or 106), thelocation of the user terminal (e.g., 107 or 108) or mobile device (e.g.,105 or 106) indicates the location of the user. The search engine (111)stores the location of the searcher with the corresponding query in thequery logs (121) and/or provides the location of the searcher to thelocation disambiguation engine (109) to resolve location ambiguity.

FIG. 2 shows a method and system to generate a location model accordingto one embodiment. In FIG. 2, a dataset generator (131) and a trainingengine (135) generate the location model (127) from input data such asquery logs (121), response logs (123), location attributes (128) andquery context data (129) in an automated way.

In FIG. 2, the dataset generator (131) generates a training dataset(133); and the training engine (135) uses the training dataset (133) toestablish the location model (127) for the location disambiguationengine (109) using a machine learning technique.

In one embodiment, the dataset generator (131) computes the values oftraining variables of the training dataset (133) from the input datasuch as query logs (121), response logs (123), location attributes (128)and query context data (129).

In one embodiment, the location attributes (128) include features oflocations that are independent of search queries recorded in the querylogs (121).

In one embodiment, the query context data (129) includes featuresrelated to the context of the search queries recorded in the query logs(121), such as the locations of the searchers, locations of businessesselected by the searchers prior to the submission of the search queries,interests of the searchers, etc.

FIG. 3 illustrates training features for a location model according toone embodiment. In FIG. 3, the training variables (147) are based onfeatures from one or more of the following categories: location features(141), personalization features (143), and search features (145).

In one embodiment, location features (141) are associated with a givenlocation. In one embodiment, location features (141) for training thelocation model (127) are independent of prior searches or queries foundin a search engine log (e.g., 121). Examples of location features (141)includes area, population, population density, demographic data,popularity, number of businesses contained within the given region,entity feature type, search hits, etc.

In one embodiment, personalization features (143) help personalize alocation disambiguation algorithm based on characteristics of searchers(e.g., the submitters of the local searchers or queries). In oneembodiment, personalization features (143) for training the locationmodel (127) are dependent upon the searchers of the queries found in thesearch engine log (e.g., 121). Examples of personalization features(143) include current user location (e.g., as derived from the GPScoordinates or IP address), past searched locations, past locations ofthe selected businesses by the respective searchers, travel speed anddirection of the searchers, interests of the searchers, home townlocations of the searchers, etc.

In one embodiment, search features (145) are related to the context ofsearches. In one embodiment, search features (145) for training thelocation model (127) are dependent upon the search queries found in thesearch engine log (e.g., 121). In one embodiment, search features (145)for training the location model (127) are dependent upon the searchqueries found in the search engine log (e.g., 121) but are independentof the respective searchers submitting the search queries. Examples ofsearch features (145) include co-occurrence frequency of search topicterms and location terms, time of the day, day of the month or week,month of the year, etc.

In one embodiment, the training features include:

the distance between the location candidate and the location of thedevice that submits the search query;

the number of businesses contained within a given location candidate;and

the number of search hits provided by a search engine for a givenlocation candidate.

For example, when a given location candidate is “San Francisco”+“CA,”the training features include the distance between the number ofbusinesses in “San Francisco, CA” and the number of search hits when theterm “San Francisco CA” is entered in search engine (111) or anothersearch engine. In one embodiment, the search hits provide a generalindication about the popularity of the location candidate. Otherindicators of popularity can also be used.

FIG. 4 shows a method and system to generate target values of a trainingdataset for a location model according to one embodiment. In oneembodiment, the dataset generator (131) of FIG. 2 includes a filter(151), an ambiguous query generator (154), a location engine (156), anda comparator (158).

In one embodiment, the filter (151) identifies the unambiguous queries(152) from the query logs (121) and the response logs (123). In oneembodiment, each of the unambiguous queries (152) has results from whichthe respective searcher has selected at least one item (e.g., a businesslisting, an advertisement, etc.), where the selection of the itemconfirms the geographical intent of the query. Thus, the confirmedgeographical intent provides the answers (153) to ambiguous queries(155).

In one embodiment, the unambiguous queries (152) are based on queries inthe query logs (121) that have unambiguous location references. In oneembodiment, some of the unambiguous queries (152) are based on queriesin the query logs (121) that have ambiguous location references, whereuser selections from search results identify the correspondingunambiguous location references.

In FIG. 4, the ambiguous query generator (154) generates the ambiguousqueries (155) from the unambiguous queries (152).

In one embodiment, the ambiguous query generator (154) generates theambiguous queries (155) from the unambiguous queries (152) by removingat least one component of the unambiguous location reference.

For example, in one embodiment, the ambiguous query generator (154)breaks each of the unambiguous location references into two parts: (1)name and (2) state constraint. For example, “san jose, ca” is brokeninto “San Jose” (name) and “CA” (state constraint). Removing the stateconstraint provides an ambiguous location reference for the respectiveambiguous query (155).

In one embodiment, the location engine (156) identifies a set oflocation candidates (157) for each of the ambiguous queries (155). Inone embodiment, the location engine (156) determines the locationcandidates (157) based on solely the ambiguous location reference of thecorresponding query. In another embodiment, the location engine (156)determines the location candidates (157) based on not only the ambiguouslocation reference of the corresponding query, but also other elementsof the ambiguous query, such as the topic terms. In one embodiment, thelocation engine (156) ranks the location candidates and provides no morethan a predetermined number of top location candidates.

In FIG. 4, the comparator (158) compares the location candidates (157)with the respective answers (153) to generate the target values (159) ofa target variable. The training engine (135) trains the location model(127) to optimize the performance of the target variable.

In one embodiment, the query context data (129) includes an indicationof whether a query is from a mobile device (e.g., 105 or 106), or from afixed-location device, such as a desktop device (e.g., 107 or 108).

In one embodiment, the dataset generator (131) generates separatetraining datasets (e.g., 133) for mobile queries and non-mobile queriesand thus generates separate location models (e.g., 127) for mobilequeries and non-mobile queries.

In one embodiment, the indication of whether a query is a mobile ornon-mobile query is a training feature for establishing a unifiedlocation model (127) for both mobile queries and non-mobile queries.

In one embodiment, the location model (127) is based on a decision treethat maps observations about an item (e.g., values of trainingvariables) to conclusions about the item's target value (e.g., values ofa target variable).

In a decision tree, leaves represent classifications or predictions; andbranches represent conjunctions of features that lead to thoseclassifications. A supervised learning technique, such as those in L.Breiman, J. Friedman, C. J. Stone, and R. Olshen (“Classification andRegression Trees,” Chapman and Hall/CRC, 1984) and I. Jenhani, N. B.Amor, and Z. Elouedi (“Decision Trees as Possibilistic Classifiers,”International Journal of Approximate Reasoning Vol. 48, No. 3, 2008, pp.784-807), can learn the decision trees from training data.

In one embodiment, a machine learning technique is to start a decisiontree by selecting a root node (decision variable and split point) usingpurity measures such as information gain or means squared error toselect both the variable and split point. Subsequently, the techniqueadds child nodes until a node becomes pure (consists of one class) orthe number of examples reaching that node becomes small. The process ofadding nodes generates the decision tree.

The modeling ability of a decision tree can be greatly enhanced throughcombining multiple decision trees in the form of committees or ensemblesof decision trees. One simple example of such an ensemble is to learnmultiple decision trees, each one trained on a different subset of thetraining data (for example). For classification of an unlabeledinstance, each tree assigns a class using the usual decision inferencestrategy. Subsequently, a majority rule assigns the class with thehighest number of votes to the example being labeled.

A more principled approach to constructing ensembles of decision treesis stochastic gradient boosted decision trees (GBDT). GBDT is anadditive classification (or regression model) consisting of an ensembleof trees, fitted to current residuals (gradient of the loss function),in a forward stepwise manner. In one embodiment, training via GBDTincludes fitting an ensemble of decision trees, each of them beingtrained separately in a sequential manner. Each iteration fits adecision to the residuals left by the classifier of the previousiteration. Prediction is accomplished by adding the predictions of eachclassifier. Reducing the shrinkage (learning rate) parameter helpsprevent over-fitting and has a smoothing effect but increases thelearning time. J. H. Friedman (“Greedy Function Approximation: AGradient Boosting Machine,” The Annals of Statistics Vol. 29, No. 5,2001, pp. 1189-1232; and “Stochastic Gradient Boosting,” ComputationalStatistics & Data Analysis, Vol. 38, No. 4, 2002, pp. 367-378) and T.Hastie, R. Tibshirani, and J. Friedman, (“The Elements of StatisticalLearning: Data Mining, Inference, and Prediction,” Springer-Verlag, NewYork, 2001) provide details on GBDT, the disclosures of which areincorporated herein by reference.

FIG. 5 shows a method to resolve location ambiguity according to oneembodiment. In one embodiment, a computing apparatus includes a datasetgenerator (131), a training engine (135) and a location disambiguationengine (109). The computing apparatus has a configuration for thefunctionalities of generating (201) a training dataset (133) using querylogs (e.g., 121) and response data (e.g., 123) of a search engine (e.g.,111) (e.g., using the dataset generator (131)), applying (203) a machinelearning technique to the training dataset (133) to generate a locationdisambiguation model (e.g., 127) (e.g., using the training engine(135)), and resolving (205) location ambiguity in search queries usingthe location disambiguation model (127) (e.g., using the locationdisambiguation engine (109)).

In one embodiment, the computing apparatus further includes the searchengine (111) to receive a search query having location ambiguity; andthe location disambiguation engine (109) identifies an unambiguouslocation for the search query to resolve the location ambiguity usingthe location model (127). The search engine (111) determines a searchresult corresponding to the search query directed to the unambiguouslocation and provides the search result as a response to the searchquery having location ambiguity.

In one embodiment, the machine learning technique includes a decisiontree learning technique, such as a gradient boosted decision treelearning technique.

In one embodiment, the training dataset (133) has at least one locationfeature (141), at least one personalization feature (143), and at leastone search feature (145).

In one embodiment, the training dataset (133) has training featuresincluding at least one of: location features (141), personalizationfeatures (143), and search features (145).

In one embodiment, the location features (141) are independent ofqueries recorded in the query logs (121), such as area, population,population density, demography, popularity, and business population of ageographic location/area.

In one embodiment, the personalization features (143) are related tosearchers of the queries recorded in the query logs (121), such assearcher location, searcher traveling direction, searcher travelingspeed, searcher searched location, and location of searcher selectedbusinesses.

In one embodiment, the search features (145) are independent ofsearchers of the queries recorded in the query logs (121), such astopic-location co-occurrence frequency, and time, day, and month of thesearches.

In one embodiment, the training dataset (133) includes training featuressuch as search hit values of the location candidates (157), businesscount values of the location candidates (157), and distances between thelocation candidates (157) and the locations of the searcherscorresponding to the queries recorded in the query logs (121).

In one embodiment, the computing apparatus includes a filter (151) tofilter the query logs (121) to identify first queries (e.g., 152)without location ambiguity, an ambiguous query generator (154) togenerate, from the first queries (e.g., 152), second queries (155)having location ambiguity, a location engine (156) to identify locationcandidates (157) for the second queries (155), and a comparator tocompare unambiguous locations (e.g., 153) specified in the first queries(e.g., 152) and corresponding location candidates (157) for the secondqueries (e.g., 155) to generate training targets (159).

In one embodiment, the computing apparatus includes a data storagedevice (e.g., 113) to store the query logs (121) and the response logs(123), which are used to generate the training dataset (133).

In one embodiment, the training dataset (133) includes at least onetraining feature independent of queries recorded in the query logs (121)(e.g., location features (141)) and at least one training featureindependent of searchers (e.g., search features (145)).

FIG. 6 shows a data processing system to implement various embodiments.While FIG. 6 illustrates various components of a computer system, it isnot intended to represent any particular architecture or manner ofinterconnecting the components. Some embodiments may use other systemsthat have fewer or more components than those shown in FIG. 6.

In one embodiment, each of the user terminals (e.g., 107, . . . , 108),the mobile devices (e.g., 105, . . . , 106), the search engine (111),the location disambiguation engine (109), the database (113), thetraining engine (135), and the dataset generator (131), or theircomponents, such as the filter (151), the ambiguous query generator(154), the location engine (156), the comparator (158) or combinationsor subsets, can be a data processing system, with more or lesscomponents, as illustrated in FIG. 6.

In FIG. 6, the data processing system (301) includes an inter-connect(302) (e.g., bus and system core logic), which interconnects amicroprocessor(s) (303) and memory (308). The microprocessor (303) iscoupled to cache memory (304) in the example of FIG. 6.

The inter-connect (302) interconnects the microprocessor(s) (303) andthe memory (308) together and also interconnects them to a displaycontroller, a display device (307), and to peripheral devices such asinput/output (I/O) devices (305) through an input/output controller(s)(306).

Typical I/O devices include mice, keyboards, modems, network interfaces,printers, scanners, video cameras and other devices which are well knownin the art. In some embodiments, when the data processing system is aserver system, some of the I/O devices, such as printer, scanner, mice,and/or keyboards, are optional.

The inter-connect (302) may include one or more buses connected to oneanother through various bridges, controllers and/or adapters. In oneembodiment, the I/O controller (306) includes a USB (Universal SerialBus) adapter for controlling USB peripherals, and/or an IEEE-1394 busadapter for controlling IEEE-1394 peripherals.

The memory (308) may include ROM (Read Only Memory), volatile RAM(Random Access Memory), and non-volatile memory, such as hard drive,flash memory, etc.

Volatile RAM is typically implemented as dynamic RAM (DRAM), whichrequires power continually in order to refresh or maintain the data inthe memory. Non-volatile memory is typically a magnetic hard drive, amagnetic optical drive, an optical drive (e.g., a DVD RAM), or othertype of memory system that maintains data even after power is removedfrom the system. The non-volatile memory may also be a random accessmemory.

The non-volatile memory can be a local device coupled directly to therest of the components in the data processing system. A non-volatilememory that is remote from the system, such as a network storage devicecoupled to the data processing system through a network interface suchas a modem or Ethernet interface, can also be used.

In this description, various functions and operations may be describedas being performed by or caused by software code to simplifydescription. However, those skilled in the art will recognize that whatis meant by such expressions is that the functions result from executionof the code/instructions by a processor, such as a microprocessor.Alternatively, or in combination, the functions and operations can beimplemented using special purpose circuitry, with or without softwareinstructions, such as using Application-Specific Integrated Circuit(ASIC) or Field-Programmable Gate Array (FPGA). Embodiments can beimplemented using hardwired circuitry without software instructions, orin combination with software instructions. Thus, the techniques arelimited neither to any specific combination of hardware circuitry andsoftware, nor to any particular source for the instructions executed bythe data processing system.

While some embodiments can be implemented in fully functioning computersand computer systems, various embodiments are capable of beingdistributed as a computing product in a variety of forms and are capableof being applied regardless of the particular type of machine orcomputer-readable media used to actually effect the distribution.

At least some aspects disclosed can be embodied, at least in part, insoftware. That is, the techniques may be carried out in a computersystem or other data processing system in response to its processor,such as a microprocessor, executing sequences of instructions containedin a memory, such as ROM, volatile RAM, non-volatile memory, cache or aremote storage device.

Routines executed to implement the embodiments may be implemented aspart of an operating system or a specific application, component,program, object, module or sequence of instructions referred to as“computer programs.” The computer programs typically include one or moreinstructions set at various times in various memory and storage devicesin a computer, and that, when read and executed by one or moreprocessors in a computer, cause the computer to perform operationsnecessary to execute elements involving the various aspects.

A machine readable medium can be used to store software and data, whichwhen executed by a data processing system, causes the system to performvarious methods. The executable software and data may be stored invarious places including, for example, ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data may be storedin any one of these storage devices. Further, the data and instructionscan be obtained from centralized servers or peer to peer networks.Different portions of the data and instructions can be obtained fromdifferent centralized servers and/or peer to peer networks at differenttimes and in different communication sessions or in the samecommunication session. The data and instructions can be obtained inentirety prior to the execution of the applications. Alternatively,portions of the data and instructions can be obtained dynamically, justin time, when needed for execution. Thus, it is not required that thedata and instructions be on a machine readable medium in entirety at aparticular instance of time.

Examples of computer-readable media include, but are not limited to,recordable and non-recordable type media such as volatile andnon-volatile memory devices, read only memory (ROM), random accessmemory (RAM), flash memory devices, floppy and other removable disks,magnetic disk storage media, optical storage media (e.g., Compact DiskRead-Only Memory (CD ROMS), Digital Versatile Disks (DVDs), etc.), amongothers.

In general, a tangible machine-readable medium includes any apparatusthat provides (e.g., stores and/or transmits) information in a formaccessible by a machine (e.g., a computer, network device, personaldigital assistant, manufacturing tool, any device with a set of one ormore processors, etc.).

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement the techniques. Thus, thetechniques are neither limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system.

Although some of the drawings illustrate a number of operations in aparticular order, operations that are not order-dependent may bereordered and other operations may be combined or broken out. While somereordering or other groupings are specifically mentioned, others will beapparent to those of ordinary skill in the art and so do not present anexhaustive list of alternatives. Moreover, it should be recognized thatthe stages could be implemented in hardware, firmware, software or anycombination thereof.

In the foregoing specification, the disclosure has been described withreference to specific exemplary embodiments thereof. It will be evidentthat various modifications may be made thereto without departing fromthe broader spirit and scope as set forth in the following claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative sense rather than a restrictive sense.

What is claimed is:
 1. A method, comprising: receiving, at a computingdevice from a searcher, a search query having location ambiguity;generating, by the computing device, a location model using a machinelearning technique, the location model generated, at least in part,based on a personalization feature, wherein the personalization featurecomprises a past location searched by the searcher; identifying, by thecomputing device using the location model, an unambiguous location forthe search query to resolve the location ambiguity; determining, by thecomputing device, a search result corresponding to the search querydirected to the unambiguous location; and providing, by the computingdevice, the search result as a response to the search query havinglocation ambiguity.
 2. The method of claim 1, further comprisinggenerating the location model, at least in part, from a query log usingthe machine learning technique.
 3. The method of claim 1, wherein themachine learning technique comprises a decision tree learning technique.4. The method of claim 1, wherein the machine learning techniquecomprises a gradient boosted decision tree learning technique.
 5. Themethod of claim 1, wherein generating the location model based, at leastin part, on the personalization feature comprises generating a trainingdataset comprising the personalization feature.
 6. The method of claim5, wherein the training dataset further comprises a location feature,and a search feature.
 7. The method of claim 1, further comprisingdetermining that the search query is received from the searcher via amobile device, wherein generating the location model comprisesgenerating a first location model for queries received from mobiledevices and generating a second location model for queries received fromfixed-location devices, and wherein identifying the unambiguous locationusing the location model comprises identifying the unambiguous locationusing the first location model based on the search query being receivedfrom the searcher via the mobile device.
 8. The method of claim 6,wherein the location feature is independent of queries recorded in aquery log.
 9. The method of claim 8, wherein the location featurecomprises at least one of: area, population, population density,demography, popularity, or business population.
 10. The method of claim8, wherein the personalization feature is related to searchers of thequeries recorded in the query log.
 11. The method of claim 10, whereinthe personalization feature further comprises at least one of: alocation of the searcher, a direction being traveled by the searcher, aspeed being traveled by the searcher, or a location of a businessselected by the searcher.
 12. The method of claim 8, wherein the searchfeature is independent of searchers of the queries recorded in the querylog.
 13. The method of claim 12, wherein the search feature comprises atleast one of: topic-location co-occurrence frequency, time, day, ormonth.
 14. The method of claim 5, wherein generating the trainingdataset comprises: filtering a query log to identify a first query, thefirst query specifying an unambiguous location; generating, from thefirst query, a second query having location ambiguity by removing acomponent of the first query; identifying a location candidate for thesecond query; and generating a training target based on comparing theunambiguous location specified in the first query and the locationcandidate for the second query.
 15. A non-transitory computer readablemedium storing instructions which, when executed on a computer, causethe computer to perform operations comprising: receiving a search queryhaving location ambiguity; generating a location model using a machinelearning technique, the location model generated, at least in part,based on a personalization feature, wherein the personalization featurecomprises a past location searched by the searcher; identifying, usingthe location model, an unambiguous location for the search query toresolve the location ambiguity; determining a search resultcorresponding to the search query directed to the unambiguous location;and providing the search result as a response to the search query havinglocation ambiguity.
 16. The non-transitory computer readable medium ofclaim 15, wherein the operations further comprise determining that thesearch query is received from the searcher via a mobile device, whereingenerating the location model comprises generating a first locationmodel for queries received from mobile devices and generating a secondlocation model for queries received from fixed-location devices, andwherein identifying the unambiguous location using the location modelcomprises identifying the unambiguous location using the first locationmodel based on the search query being received from the searcher via themobile device.
 17. A system, comprising: a processor; and a memorystoring instructions that, when executed by the processor, cause theprocessor to perform operations comprising receiving a search queryhaving location ambiguity, generating a location model using a machinelearning technique, the location model generated, at least in part,based on a personalization feature, wherein the personalization featurecomprises a past location searched by the searcher, identifying, usingthe location model, an unambiguous location for the search query toresolve the location ambiguity, determining a search resultcorresponding to the search query directed to the unambiguous location,and providing the search result as a response to the search query havinglocation ambiguity.
 18. The system of claim 17, wherein the memorystores further instructions that, when executed by the processor, causethe processor to perform operations comprising determining that thesearch query is received from the searcher via a mobile device, whereingenerating the location model comprises generating a first locationmodel for queries received from mobile devices and generating a secondlocation model for queries received from fixed-location devices, andwherein identifying the unambiguous location using the location modelcomprises identifying the unambiguous location using the first locationmodel based on the search query being received from the searcher via themobile device.
 19. The system of claim 17, wherein generating thelocation model based, at least in part, on the personalization featurecomprises generating a training dataset comprising the personalizationfeature.
 20. The system of claim 19, wherein generating the trainingdataset comprises: filtering a query log to identify a first query, thefirst query specifying an unambiguous location; generating, from thefirst query, a second query having location ambiguity by removing acomponent of the first query; identifying a location candidate for thesecond query; and generating a training target based on comparing theunambiguous location specified in the first and the location candidatefor the second query.